# model
model_name = 'conmh'
use_checkpoint = None
feature_size = 4096
hidden_size = 256
max_frames = 25
nbits = 64
transformer_type = 'small'

# dataset
dataset = 'yfcc'
workers = 1
batch_size = 128
mask_prob = 0.75

# train
seed = 1
num_epochs = 45
a = 1.0
temperature = 0.5
tau_plus = 0.1
train_num_sample = 409788

# test
test_batch_size = 128
test_num_sample = 101256
query_num_sample = 1000

# optimizer
optimizer_name = 'Adam'
schedule = 'StepLR'
lr = 1e-4
min_lr = 1e-5
lr_decay_rate = 20
lr_decay_gamma = 0.9
weight_decay = 0.0

# path
data_root = '/data/dataset/yfcc/'
home_root = '/data/conmh/'

train_feat_path = [data_root + 'yfcc_train_feats_' + str(i) + '.h5' for i in range(1,10)]
test_feat_path = [data_root + 'yfcc_test_feats_' + str(i) + '.h5' for i in range(1,3)]
label_path = [data_root + 'yfcc_test_labels_' + str(i) + '.mat' for i in range(1,3)]
query_feat_path = [data_root + 'query1000_feats.h5']
query_label_path = [data_root + 'query1000_labels.mat']

save_dir = home_root + 'saved_model/' + model_name + '_' + dataset
file_path = save_dir + '_' + str(nbits) + 'bit'

